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Online Computation of Fastest Path in TimeDependent Spatial Networks
, 2011
"... The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. Howe ..."
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The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. However, with realworld spatial networks the edge traveltimes are timedependent, where the arrivaltime to an edge determines the actual traveltime on the edge. In this paper, we study the online computation of fastest path in timedependent spatial networks and present a technique which speedsup the path computation. We show that our fastest path computation based on a bidirectional timedependent A * search significantly improves the computation time and storage complexity. With extensive experiments using real datasets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.
On the Complexity of TimeDependent Shortest Paths
"... We investigate the complexity of shortest paths in timedependent graphs, in which the costs of edges vary as a function of time, and as a result the shortest path between two nodes s and d can change over time. Our main result is that when the edge cost functions are (polynomialsize) piecewise line ..."
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We investigate the complexity of shortest paths in timedependent graphs, in which the costs of edges vary as a function of time, and as a result the shortest path between two nodes s and d can change over time. Our main result is that when the edge cost functions are (polynomialsize) piecewise linear, the shortest path from s to d can change Θ(log n) n times, settling a severalyearold conjecture of Dean [Technical Reports, 1999, 2004]. We also show that the complexity is polynomial if the slopes of the linear function come from a restricted class, present an outputsensitive algorithm for the general case, and describe a scheme for a (1 + ɛ)approximation of the travel time function in nearquadratic space. Finally, despite the fact that the arrival time function may have superpolynomial complexity, we show that a minimum delay path for any departure time interval can be computed in polynomial time. 1
Traffic Management as a Service: The Traffic Flow Pattern Classification Problem
"... Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To ach ..."
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Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To achieve such goal these systems should account for both estimated and realtime traffic congestion states, but obtaining reliable traffic congestion estimations for all the streets/avenues in a city for the different times of the day, for every day in a year, is a complex task. Modeling such a tremendous amount of data can be timeconsuming and, additionally, centralized computation of optimal routes based on such timedependencies has very high data processing requirements. In this paper we approach this problem through a heuristic to considerably reduce the modeling effort while maintaining the benefits of timedependent traffic congestion modeling. In particular, we propose grouping streets by taking into account real traces describing the daily traffic pattern. The effectiveness of this heuristic is assessed for the city of Valencia, Spain, and the results obtained show that it is possible to reduce the required number of daily traffic flow patterns by a factor of 4210 while maintaining the essence of timedependent modeling requirements.
Distance Oracles for TimeDependent Networks
"... Abstract. We present the first approximate distance oracle for sparse directed networks with timedependent arctraveltimes determined by continuous, piecewise linear, positive functions possessing the FIFO property. Our approach precomputes (1 + ε)−approximate distance summaries from selected la ..."
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Abstract. We present the first approximate distance oracle for sparse directed networks with timedependent arctraveltimes determined by continuous, piecewise linear, positive functions possessing the FIFO property. Our approach precomputes (1 + ε)−approximate distance summaries from selected landmark vertices to all other vertices in the network, and provides two sublineartime query algorithms that deliver constant and (1+σ)−approximate shortesttraveltimes, respectively, for arbitrary origindestination pairs in the network. Our oracle is based only on the sparsity of the network, along with two quite natural assumptions about traveltime functions which allow the smooth transition towards asymmetric and timedependent distance metrics. 1